PERF: faster groupby diff

asv_bench/benchmarks/groupby.py

@@ -405,7 +405,7 @@ class GroupByMethods:
 
     param_names = ["dtype", "method", "application", "ncols"]
     params = [
-        ["int", "float", "object", "datetime", "uint"],
+        ["int", "int16", "float", "object", "datetime", "uint"],
         [
             "all",
             "any",
@@ -417,6 +417,7 @@ class GroupByMethods:
             "cumprod",
             "cumsum",
             "describe",
+            "diff",
             "ffill",
             "first",
             "head",
@@ -478,7 +479,7 @@ def setup(self, dtype, method, application, ncols):
         values = rng.take(taker, axis=0)
         if dtype == "int":
             key = np.random.randint(0, size, size=size)
-        elif dtype == "uint":
+        elif dtype in ("int16", "uint"):
             key = np.random.randint(0, size, size=size, dtype=dtype)
         elif dtype == "float":
             key = np.concatenate(

doc/source/whatsnew/v1.5.0.rst

@@ -253,6 +253,7 @@ Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 - Performance improvement in :meth:`.GroupBy.transform` for some user-defined DataFrame -> Series functions (:issue:`45387`)
 - Performance improvement in :meth:`DataFrame.duplicated` when subset consists of only one column (:issue:`45236`)
+- Performance improvement in :meth:`.GroupBy.diff` (:issue:`16706`)
 - Performance improvement in :meth:`.GroupBy.transform` when broadcasting values for user-defined functions (:issue:`45708`)
 - Performance improvement in :meth:`.GroupBy.transform` for user-defined functions when only a single group exists (:issue:`44977`)
 - Performance improvement in :meth:`MultiIndex.get_locs` (:issue:`45681`)

pandas/core/groupby/groupby.py

@@ -3491,6 +3491,47 @@ def shift(self, periods=1, freq=None, axis=0, fill_value=None):
         )
         return res
 
+    @final
+    @Substitution(name="groupby")
+    @Appender(_common_see_also)
+    def diff(self, periods: int = 1, axis: int = 0) -> Series | DataFrame:
+        """
+        First discrete difference of element.
+
+        Calculates the difference of each element compared with another
+        element in the group (default is element in previous row).
+
+        Parameters
+        ----------
+        periods : int, default 1
+            Periods to shift for calculating difference, accepts negative values.
+        axis : axis to shift, default 0
+            Take difference over rows (0) or columns (1).
+
+        Returns
+        -------
+        Series or DataFrame
+            First differences.
+        """
+        if axis != 0:
+            return self.apply(lambda x: x.diff(periods=periods, axis=axis))
+
+        obj = self._obj_with_exclusions
+        shifted = self.shift(periods=periods, axis=axis)
+
+        # GH45562 - to retain existing behavior and match behavior of Series.diff(),
+        # int8 and int16 are coerced to float32 rather than float64.
+        dtypes_to_f32 = ["int8", "int16"]
+        if obj.ndim == 1:
+            if obj.dtype in dtypes_to_f32:
+                shifted = shifted.astype("float32")
+        else:
+            to_coerce = [c for c, dtype in obj.dtypes.items() if dtype in dtypes_to_f32]
+            if len(to_coerce):
+                shifted = shifted.astype({c: "float32" for c in to_coerce})
+
+        return obj - shifted
+
     @final
     @Substitution(name="groupby")
     @Appender(_common_see_also)

pandas/tests/groupby/test_groupby_shift_diff.py

@@ -69,7 +69,7 @@ def test_group_shift_lose_timezone():
     tm.assert_series_equal(result, expected)
 
 
-def test_group_diff_real(any_real_numpy_dtype):
+def test_group_diff_real_series(any_real_numpy_dtype):
     df = DataFrame(
         {"a": [1, 2, 3, 3, 2], "b": [1, 2, 3, 4, 5]},
         dtype=any_real_numpy_dtype,
@@ -82,6 +82,29 @@ def test_group_diff_real(any_real_numpy_dtype):
     tm.assert_series_equal(result, expected)
 
 
+def test_group_diff_real_frame(any_real_numpy_dtype):
+    df = DataFrame(
+        {
+            "a": [1, 2, 3, 3, 2],
+            "b": [1, 2, 3, 4, 5],
+            "c": [1, 2, 3, 4, 6],
+        },
+        dtype=any_real_numpy_dtype,
+    )
+    result = df.groupby("a").diff()
+    exp_dtype = "float"
+    if any_real_numpy_dtype in ["int8", "int16", "float32"]:
+        exp_dtype = "float32"
+    expected = DataFrame(
+        {
+            "b": [np.nan, np.nan, np.nan, 1.0, 3.0],
+            "c": [np.nan, np.nan, np.nan, 1.0, 4.0],
+        },
+        dtype=exp_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "data",
     [